# Automating order entry from inbox to ERP > A private-equity-backed European manufacturer of engineered wood products ยท Industrial & Aviation A private-equity-backed European manufacturer of engineered wood products relied on a dedicated team to interpret and rekey emailed PDF orders into SAP by hand. QuantSpark built a generative AI pipeline that automates half of customer orders end to end. ## At a glance - **50%** of customer orders automated end to end - Engagement: 4 weeks ## What was the problem? Customer orders arrived by email as PDF attachments with no template conformity: multiple languages, varied layouts, and product descriptions that deviated from official SKUs. A dedicated team had to interpret and rekey each order into SAP by hand. The process was slow, prone to typos and to missed fields that caused fulfilment problems, and could not scale as volumes grew. ## What did QuantSpark do? QuantSpark ran ideation workshops to prioritise use cases, then a focused four-week feasibility study on order management, followed by a proof-of-concept and an MVP under our proof-of-concept to MVP to build model. The solution is a three-step pipeline: a multi-modal generative AI model extracts order and line-item detail from each PDF; classification algorithms trained on historic order descriptions predict a unique SKU per line; and historic purchase data fills the remaining fields. When GPT-4o was released mid-study, the team integrated it within 24 hours. The MVP was deployed to a production-state Azure environment with automated SAP ingestion, producing EDI-ready XML and routing orders to automatic processing or manual review by confidence. ## What changed? A four-week feasibility study automated 50% of customer orders end to end, from PDF to product identification, exceeding its accuracy targets: 50% order-detail accuracy against a 40% target and 40% product-detail accuracy against a 30% target. Adopting GPT-4o cut inference costs by roughly half. At MVP stage, field-level accuracy exceeded targets on the core fields (split order 92%, delivery plant 90%, quantity 91%, material 83%), with delivery date the remaining weaker field at 55%. --- Canonical page: https://quantspark.ai/case-studies/genai-order-automation-manufacturing More about QuantSpark: https://quantspark.ai/llms.txt